{"title":"State estimation of fractional order network system based on modified fractional order Kalman filter","authors":"Yi Wang, Yonghui Sun, Zhi-nong Wei, Guo-qiang Sun","doi":"10.1109/CCDC.2017.7978076","DOIUrl":null,"url":null,"abstract":"Accurate state estimation is essential for the application of fractional order network system. In order to provide a more reliable state estimation method to address the inevitably data packet dropout problem of network control system, in this paper, a modified fractional order Kalman filter is developed by combining of the conventional fractional order Kalman filter and the proposed adaptive compensation approach. Simulation results are provided to demonstrate that the proposed method possesses much better estimation performances than the conventional fractional order Kalman filter.","PeriodicalId":6588,"journal":{"name":"2017 29th Chinese Control And Decision Conference (CCDC)","volume":"2 1","pages":"112-116"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 29th Chinese Control And Decision Conference (CCDC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCDC.2017.7978076","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Accurate state estimation is essential for the application of fractional order network system. In order to provide a more reliable state estimation method to address the inevitably data packet dropout problem of network control system, in this paper, a modified fractional order Kalman filter is developed by combining of the conventional fractional order Kalman filter and the proposed adaptive compensation approach. Simulation results are provided to demonstrate that the proposed method possesses much better estimation performances than the conventional fractional order Kalman filter.